2 citations · 3 across the 17 of their papers we have counts for
12 papers · 1 filter
Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling
Panagiotis Theodoropoulos, Nan Jiang, Xintong Duan +4
Power-sharpened sampling is an inference-time alternative to reinforcement-learning (RL) post-training for enhancing reasoning in large language models (LLMs). High-probability seq…
How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off
Waïss Azizian, Ali Hasan
The factors driving the performance of in-context learning (ICL) in large language models (LLMs) remain poorly understood despite ICL's surprising effectiveness, enabling models to…
Conditional Average Treatment Effect Estimation Under Hidden Confounders
Ahmed Aloui, Juncheng Dong, Ali Hasan +1
One of the major challenges in estimating conditional potential outcomes and conditional average treatment effects (CATE) is the presence of hidden confounders. Since testing for h…
Elliptic Loss Regularization
Ali Hasan, Haoming Yang, Yuting Ng +1
Regularizing neural networks is important for anticipating model behavior in regions of the data space that are not well represented. In this work, we propose a regularization tech…
Parabolic Continual Learning
Haoming Yang, Ali Hasan, Vahid Tarokh
Regularizing continual learning techniques is important for anticipating algorithmic behavior under new realizations of data. We introduce a new approach to continual learning by i…
Score-Based Metropolis-Hastings Algorithms
Ahmed Aloui, Ali Hasan, Juncheng Dong +2
In this paper, we introduce a new approach for integrating score-based models with the Metropolis-Hastings algorithm. While traditional score-based diffusion models excel in accura…
Base Models for Parabolic Partial Differential Equations
Xingzi Xu, Ali Hasan, Jie Ding +1
Parabolic partial differential equations (PDEs) appear in many disciplines to model the evolution of various mathematical objects, such as probability flows, value functions in con…
Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes
Haoming Yang, Ali Hasan, Yuting Ng +1
McKean-Vlasov stochastic differential equations (MV-SDEs) provide a mathematical description of the behavior of an infinite number of interacting particles by imposing a dependence…
Perceiver-based CDF Modeling for Time Series Forecasting
Cat P. Le, Chris Cannella, Ali Hasan +2
Transformers have demonstrated remarkable efficacy in forecasting time series data. However, their extensive dependence on self-attention mechanisms demands significant computation…
Characteristic Neural Ordinary Differential Equations
Xingzi Xu, Ali Hasan, Khalil Elkhalil +2
We propose Characteristic-Neural Ordinary Differential Equations (C-NODEs), a framework for extending Neural Ordinary Differential Equations (NODEs) beyond ODEs. While NODEs model…
Generative Archimedean Copulas
Yuting Ng, Ali Hasan, Khalil Elkhalil +1
We propose a new generative modeling technique for learning multidimensional cumulative distribution functions (CDFs) in the form of copulas. Specifically, we consider certain clas…
Learning Partial Differential Equations from Data Using Neural Networks
Ali Hasan, João M. Pereira, Robert Ravier +2
We develop a framework for estimating unknown partial differential equations from noisy data, using a deep learning approach. Given noisy samples of a solution to an unknown PDE, o…